scholarly journals CAPTURING MATHEMATICAL AND HUMAN PERCEPTIONS OF SHAPE AND FORM THROUGH MACHINE LEARNING

2021 ◽  
Vol 1 ◽  
pp. 591-600
Author(s):  
James Gopsill ◽  
Mark Goudswaard ◽  
David Jones ◽  
Ben Hicks
Keyword(s):  
Machine Learning ◽  
Product Development ◽  
Engineering Design ◽  
Daily Basis ◽  
Shape Grammars ◽  
Core Feature ◽  
Human Perceptions ◽  
Market Feedback

AbstractClassifying shape and form is a core feature of Engineering Design and one that we do this instinctively on a daily basis. Matching similar components to then reduce unique component counts, determining whether a competitors design infringes on copyright and receiving market feedback on product styling are all examples where shape and form comes into play. However, shape and form can be perceived in different ways from purely mathematical (e.g. shape grammars) to wholly subjective (e.g. market feedback) and these perceptions may not entirely agree.This paper examines the mathematical and human perceptions of shape and form through a study of classifying shapes that have been interpolated between one another, and in doing so, highlights the disparity in perceptions. Following this, the paper demonstrates how the emergent field of Machine Learning can be applied to capture mathematical and human perceptions of shape and form resulting in a means to twin this feedback into product development.

scholarly journals Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4846
Author(s):  
Dušan Marković ◽  
Dejan Vujičić ◽  
Snežana Tanasković ◽  
Borislav Đorđević ◽  
Siniša Ranđić ◽  
...  
Keyword(s):  
Machine Learning ◽  
Relative Humidity ◽  
Weather Conditions ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Lower Percentage ◽  
Timely Manner ◽  
Proposed Model ◽  
Set Up ◽  
Accuracy Of Prediction

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.

scholarly journals Computer System Integration Technology of Hydraulic Engineering Design Based on Machine Learning Algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032058
Author(s):  
Ting Liu
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Computer System ◽  
Risk Prediction ◽  
System Integration ◽  
Learning Algorithm ◽  
Hydraulic Engineering ◽  
Machine Learning Algorithm ◽  
Water Conservancy ◽  
Learning Machine

Abstract With the development of water conservancy informatization, the research on water information system integration is born, which is the need of water conservancy informatization construction at present and also an urgent problem to be solved. Based on the machine learning algorithm, combined with the actual needs of water conservancy business field, the overall framework of computer system integration for water conservancy engineering design is put forward. The overall framework includes: resource layer, comprehensive integration layer and user layer, which exchange data with configuration monitoring software by means of communication. The analytic hierarchy process in machine learning algorithm is used to construct the risk prediction index system, and the risk prediction index and initial prediction results are taken as the input and output of extreme learning machine algorithm in machine learning algorithm. The simulation results show that the prediction accuracy of this method is 94.88%, which can accurately predict the risks existing in hydraulic engineering design computer system and improve the system security.

Generating Pseudo-Data to Enhance the Performance of Classification-Based Engineering Design: A Preliminary Investigation

2020 ◽  
Author(s):  
Xianping Du ◽  
Onur Bilgen ◽  
Hongyi Xu
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Real World ◽  
Surrogate Model ◽  
Preliminary Investigation ◽  
Learning Model ◽  
Classification Model ◽  
Design Decision ◽  
Large Dataset ◽  
Machine Learning Model

Abstract Machine learning for classification has been used widely in engineering design, for example, feasible domain recognition and hidden pattern discovery. Training an accurate machine learning model requires a large dataset; however, high computational or experimental costs are major issues in obtaining a large dataset for real-world problems. One possible solution is to generate a large pseudo dataset with surrogate models, which is established with a smaller set of real training data. However, it is not well understood whether the pseudo dataset can benefit the classification model by providing more information or deteriorates the machine learning performance due to the prediction errors and uncertainties introduced by the surrogate model. This paper presents a preliminary investigation towards this research question. A classification-and-regressiontree model is employed to recognize the design subspaces to support design decision-making. It is implemented on the geometric design of a vehicle energy-absorbing structure based on finite element simulations. Based on a small set of real-world data obtained by simulations, a surrogate model based on Gaussian process regression is employed to generate pseudo datasets for training. The results showed that the tree-based method could help recognize feasible design domains efficiently. Furthermore, the additional information provided by the surrogate model enhances the accuracy of classification. One important conclusion is that the accuracy of the surrogate model determines the quality of the pseudo dataset and hence, the improvements in the machine learning model.

Introducing the Industrial Product Realization Process Into Mechanical Engineering Design Education

1995 ◽  
Author(s):  
Vance D. Browne
Keyword(s):  
Product Development ◽  
Engineering Design ◽  
Design Education ◽  
Process Development ◽  
Project Planning ◽  
Concept Generation ◽  
Engineering Project ◽  
Engineering Design Education ◽  
Realization Process ◽  
Product Realization

Abstract The process by which new products are brought to market — the product realization process, or PRP — can be introduced in engineering design education. In industry, the PRP has been evolving to concurrent engineering and product teams. The PRP includes components such as concept generation, analysis, manufacturing process development and customer interaction. Also, it involves the sequencing of the components and their connections which includes teamwork, project planning, meetings, reports and presentations. A capstone senior engineering project, along with classroom lectures and presentations can be structured to provide knowledge and experience to the students in many of the PRP components and the connections. This paper will give an overview of the PRP and a project/lecture structure at the author’s university. The instructor recently joined the academic ranks after years in industry with responsibility for directing product development and R&D and for leading product development teams.

Combining Lean and Six Sigma in the context of Systems Engineering design

2015 ◽  
Vol 6 (4) ◽  
pp. 290-312 ◽  
Author(s):  
TR Sreeram ◽  
Asokan Thondiyath
Keyword(s):  
Product Development ◽  
Engineering Design ◽  
Systems Engineering ◽  
Social Action ◽  
Six Sigma ◽  
Systems Design ◽  
System Level ◽  
Future Research ◽  
Content Type ◽  
Future Work

Purpose – The purpose of this paper is to present a combined framework for system design using Six Sigma and Lean concepts. Systems Engineering has evolved independently and there are numerous tools and techniques available to address issues that may arise in the design of systems. In the context of systems design, the application of Six Sigma and Lean concepts results in a flexible and adaptable framework. A combined framework is presented here that allows better visualization of the system-level components and their interactions at parametric level, and it also illuminates gaps that make way for continuous improvement. The Deming’s Plan-Do-Check-Act is the basis of this framework. Three case studies are presented to evaluate the application of this framework in the context of Systems Engineering design. The paper concludes with a summary of advantages of using a combined framework, its limitations and scope for future work. Design/methodology/approach – Six Sigma, Lean and Systems Engineering approaches combined into a framework for collaborative product development. Findings – The present framework is not rigid and does not attempt to force fit any tools or concepts. The framework is generic and allows flexibility through a plug and play type of implementation. This is important, as engineering change needs vary constantly to meet consumer demands. Therefore, it is important to engrain flexibility in the development of a foundational framework for design-encapsulating improvements and innovation. From a sustainability perspective, it is important to develop techniques that drive rationality in the decisions, especially during tradeoffs and conflicts. Research limitations/implications – Scalability of the approach for large systems where complex interactions exist. Besides, the application of negotiation techniques for more than three persons poses a challenge from a mathematical context. Future research should address these in the context of systems design using Six Sigma and Lean techniques. Practical implications – This paper provides a flexible framework for combining the three techniques based on Six Sigma, Lean and Systems Engineering. Social implications – This paper will influence the construction of agent-based systems, particularly the ones using the Habermas’s theory of social action as the basis for product development. Originality/value – This paper has not been published in any other journal or conference.

Modelling engineering information with machine learning

1996 ◽  
Vol 10 (2) ◽  
pp. 171-174 ◽  
Author(s):  
Yoram Reich
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Practical Importance ◽  
Knowledge Generation ◽  
Systematic Treatment ◽  
Learning Tasks ◽  
Engineering Problems ◽  
Engineering Information ◽  
Automated Knowledge ◽  
Tools And Methods

Since the inception of research on machine learning (ML), these techniques have been associated with the task of automated knowledge generation or knowledge reorganization. This association still prevails, as seen in this issue. When the use of ML programs began to attract researchers in engineering design, different existing tools were used to test their utility and gradually, variations of these tools and methods have sprung up. In many cases, the use of these tools was based on availability and not necessarily applicability. When we began working on ML in design, we attempted to follow a different path (Reich, 1991a; Reich & Fenves, 1992) that led to the design of Bridger (Reich & Fenves, 1995), a system for learning bridge synthesis knowledge. Subsequent experiences and further reflection led us to conclude that the process of using ML in design requires careful and systematic treatment for identifying appropriate ML programs for executing the learning tasks we wish to perform (Reich, 1991b, 1993a). Another observation was that the task of creating or reorganizing knowledge for real design tasks is outside the scope of present ML programs. Establishing the practical importance of ML techniques had to start by addressing engineering problems that could benefit from present ML programs.

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